Visualization of vessel movements

Abstract

We propose a geographical visualization to support operators of coastal surveillance systems and decision making analysts to get insights in vessel movements. For a possibly unknown area, they want to know where significant maritime areas, like highways and anchoring zones, are located. We show these features as an overlay on a map. As source data we use AIS data: Many vessels are currently equipped with advanced GPS devices that frequently sample the state of the vessels and broadcast them. Our visualization is based on density fields that are derived from convolution of the dynamic vessel positions with a kernel. The density fields are shown as illuminated height maps. Combination of two fields, with a large and small kernel provides overview and detail. A large kernel provides an overview of area usage revealing vessel highways. Details of speed variations of individual vessels are shown with a small kernel, highlighting anchoring zones where multiple vessels stop. Besides for maritime applications we expect that this approach is useful for the visualization of moving object data in general.

Visualization of Vessel Traffic

“…We are surprised that the space-time cube does not perform best too, since comparing slopes tends to be easy as well, however in this particular visualization the occlusion and viewpoint might obscure the slope, as also mentioned by the subjects in the questionnaire. It is remarkable that the space-time cube works best for the lanes task, since the density is expected to perform best here, due to the nice overview of sea lanes in [162]. Reasons why density does not excel for comparing lanes could be that: (1) due to the smoothing, variations between the number of trajectories diminish; (2) due to the spatial variation, the lane with the highest number of trajectories may not have the highest density if the lane is slightly wider than the other lanes; (3) for a small number of trajectories, which may not cover the whole lane, holes may occur as in Figure 4.2 for M fast .…”

“…In Chapters 3 to 6 we present a summarization method with various extensions and in Chapter 7 we show an explorative method by means of direct depiction and pattern extraction. Chapter 3 presents a multi-scale visualization technique [162] for showing an aggregated summary of the relation between location and duration of large amounts of movements. The contribution consists of two parts.…”

“…After the vessel density publication [162] it has been shown at the website of New Scientist [109] and had follow ups at major visualization websites like Visual Complexity [98], Infosthetics [105], and many personal blogs. The density approach has also been published in the national press, including Kijk ( Last but not least, the academic community has followed up our papers with interesting alternative approaches, which we discussed in Chapter 2.…”

“…We evaluate the performance of a dot animation, the space-time cube, and vessel density [162]. In terms of aggregated views, an animation of dots moving along the position of the objects is an aggregated view of all current locations (x , y ) for varying times t .…”

Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal

“…Montewka et al (2010) used advanced statistical and optimization methods (Monte Carlo and genetic algorithms) to present a new approach for the geometrical probability of collision estimation. Willems et al (2009) proposed a geographical visualization of AIS data to support decision-making, traffic control and coastal surveillance. This visualization is based on density fields shown as illuminated height maps.…”